research-article

Predictive Visual Analytics using Topic Composition

Authors:

Yun JangAuthors Info & Claims

VINCI '15: Proceedings of the 8th International Symposium on Visual Information Communication and Interaction

Pages 1 - 8

https://doi.org/10.1145/2801040.2801054

Published: 24 August 2015 Publication History

Abstract

Digital big data provide the vast potential of increasing effectiveness and efficiency for decision making. Since the volume of the data is enormous, the data analysis requires large amount of time and effort. It is more problematic when predictive analysis is necessary for futuristic decision making. In order for predictive analysis, there have been many studies to forecast future trends spatiotemporally. However, most of studies provide just future tendency per event using graphs or maps without contextual compositive analysis. In this paper, we present a predictive visual analytics system to provide predictive event patterns. We infer the future event evolution by combining contextually similar cases occurring in the past. We utilize social media data to detect interesting abnormal events and match the detected abnormal events within the past news media data in order to retrieve similar event patterns. Then, we extract future event patterns through compositing contextual relationship among topics included in the similar past patterns. In order to evaluate our VA system, we demonstrate two use cases in this paper and validate our system with possible predictive story lines.

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Cited By

Li QWu ZYi LSeann KQu HMa X(2020)WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat ArticlesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286777626:2(1399-1412)Online publication date: 1-Feb-2020
https://doi.org/10.1109/TVCG.2018.2867776
Seth SJohari R(2020) SNAP Intelligent Data Analysis10.1002/9781119544487.ch15(307-331)Online publication date: 2-Jun-2020
https://doi.org/10.1002/9781119544487.ch15
Yeon HKim SJang Y(2018)Predictive visual analytics of event evolution for user-created contextJournal of Visualization10.1007/s12650-016-0373-720:3(471-486)Online publication date: 24-Dec-2018
https://dl.acm.org/doi/10.1007/s12650-016-0373-7
Show More Cited By

Index Terms

Predictive Visual Analytics using Topic Composition
1. Applied computing
  1. Document management and text processing
    1. Document capture
      1. Graphics recognition and interpretation
2. Computing methodologies
  1. Computer graphics

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cover image ACM Other conferences

VINCI '15: Proceedings of the 8th International Symposium on Visual Information Communication and Interaction

August 2015

185 pages

ISBN:9781450334822

DOI:10.1145/2801040

General Chair:
Takayuki Itoh,
Program Chairs:
Paolo Bottoni,
Shigeo Takahashi

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 24 August 2015

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National Research Foundation of Korea

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VINCI '15

VINCI '15: The 8th International Symposium on Visual Information Communication and Interaction

August 24 - 26, 2015

AA, Tokyo, Japan

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VINCI '15 Paper Acceptance Rate 12 of 32 submissions, 38%;

Overall Acceptance Rate 71 of 193 submissions, 37%

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Cited By

Li QWu ZYi LSeann KQu HMa X(2020)WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat ArticlesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286777626:2(1399-1412)Online publication date: 1-Feb-2020
https://doi.org/10.1109/TVCG.2018.2867776
Seth SJohari R(2020) SNAP Intelligent Data Analysis10.1002/9781119544487.ch15(307-331)Online publication date: 2-Jun-2020
https://doi.org/10.1002/9781119544487.ch15
Yeon HKim SJang Y(2018)Predictive visual analytics of event evolution for user-created contextJournal of Visualization10.1007/s12650-016-0373-720:3(471-486)Online publication date: 24-Dec-2018
https://dl.acm.org/doi/10.1007/s12650-016-0373-7
Lu JChen WMa YKe JLi ZZhang FMaciejewski R(2018)Recent progress and trends in predictive visual analyticsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-6028-y11:2(192-207)Online publication date: 15-Dec-2018
https://dl.acm.org/doi/10.1007/s11704-016-6028-y
Zou ZXiong JHe XDai H(2018)NUPT ST-Data Miner: An Spatio-Temporal Data Analysis and Visualization SystemInformation Science and Applications 201810.1007/978-981-13-1056-0_5(41-51)Online publication date: 24-Jul-2018
https://doi.org/10.1007/978-981-13-1056-0_5
Chen SLin LYuan X(2017)Social Media Visual AnalyticsComputer Graphics Forum10.1111/cgf.1321136:3(563-587)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1111/cgf.13211
Ji WTang YMa PTang SFang ZLi XChen TZhang KKerren A(2016)A Hybrid Approach for Event Social Influence VisualizationProceedings of the 9th International Symposium on Visual Information Communication and Interaction10.1145/2968220.2968246(117-121)Online publication date: 24-Sep-2016
https://dl.acm.org/doi/10.1145/2968220.2968246

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